Blur is a key property in the perception of COVID-19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection regions, since different blur degrees cause the magnitudes change of TM on infection regions image, blur of infection regions can be captured by TM. With the above observation, a longitudinal objective quantitative evaluation method for COVID-19 disease progression based on TM is proposed. COVID-19 disease progression CT image database (COVID-19 DPID) is built to employ radiologist subjective ratings and manual contouring, which can test and compare disease progression on the CT images acquired from the same patient over time. Then the images are preprocessed, including lung automatic segmentation, longitudinal registration, slice fusion, and a fused slice image with region of interest (ROI) is obtained. Next, the gradient of a fused ROI image is calculated to represent the shape. The gradient image of fused ROI is separated into same size blocks, a block energy is calculated as quadratic sum of non-direct current moment values. Finally, the objective assessment score is obtained by TM energy-normalized applying block variances. We have conducted experiment on COVID-19 DPID and the experiment results indicate that our proposed metric supplies a satisfactory correlation with subjective evaluation scores, demonstrating effectiveness in the quantitative evaluation for COVID-19 disease progression.

中文翻译：

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基于 Tchebichef 矩的 COVID-19 胸部 CT 疾病进展纵向评估

模糊是感知 COVID-19 计算机断层扫描 (CT) 图像表现的关键属性。通常，模糊会导致边缘扩展，从而导致感染区域的形状发生变化。Tchebichef 矩 (TM) 已在形状表示中得到有效验证。直观地说，同一患者在治疗过程中随着时间的推移疾病进展表现为感染区域的不同模糊程度，由于不同的模糊程度会导致感染区域图像上TM的幅度变化，因此TM可以捕捉到感染区域的模糊。基于上述观察，提出了一种基于 TM 的 COVID-19 疾病进展纵向客观定量评价方法。COVID-19 疾病进展 CT 图像数据库 (COVID-19 DPID) 旨在采用放射科医生的主观评分和手动轮廓，它可以测试和比较从同一患者随时间获取的 CT 图像上的疾病进展。然后对图像进行预处理，包括肺自动分割、纵向配准、切片融合，得到融合后的感兴趣区域（ROI）切片图像。接下来，计算融合的 ROI 图像的梯度以表示形状。融合 ROI 的梯度图像被分成相同大小的块，一个块的能量被计算为非直流矩值的二次和。最后，通过 TM 能量归一化应用块方差获得客观评估分数。我们对 COVID-19 DPID 进行了实验，实验结果表明，我们提出的指标与主观评价分数具有令人满意的相关性，